Skip to content
Get packs

PEFT Efficient Fine-Tuning

Teaches the agent to fine-tune large language models cheaply using LoRA and QLoRA adapters

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
3 files

What it does

This skill teaches an AI agent how to adapt a large language model to a specific task without retraining all of its billions of parameters. Instead, it trains small add-on layers (adapters, often a few megabytes) on top of a base model, which makes fine-tuning possible on a single consumer GPU. The agent can write the setup code, choose LoRA settings such as rank and target layers, load and merge adapters, and suggest when QLoRA (quantized training) is the better choice.

Great for

  • Developers who want to fine-tune 7B to 70B open models on limited GPU memory
  • Teams that need several task-specific variants served from one base model
  • Data scientists comparing LoRA settings and memory trade-offs

Skip it if

  • Users with no Python or GPU experience, since the code assumes a working ML environment and access to model weights (for example gated Llama models) (lacks setup context)
  • Small models under 1B parameters, where the skill itself recommends full fine-tuning instead
  • Anyone training on sensitive data without reviewing the datasets and hosting choices, since the skill shows pushing merged models to the Hugging Face Hub

Try asking

  • “Set up a LoRA fine-tune of Llama 3.1 8B on my Dolly-style instruction dataset with a single GPU”
  • “Show me how to do QLoRA on a 70B model that fits in 24GB of VRAM”
  • “Which LoRA rank and target modules should I use for a Mistral model on a small task?”
  • “Merge my trained adapter into the base model and save it for deployment”
Step-by-step guide: how to use PEFT Efficient Fine-TuningInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    peft.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip peft.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip peft.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Start with rank 8 or 16 and alpha equal to twice the rank, then increase only if quality is lacking
  • Run print_trainable_parameters() to confirm the trainable share is small before a long training run
  • Review any code that downloads models or pushes to the Hub, and check your dataset licences before training

What's inside

  • references/advanced-usage.md
  • references/troubleshooting.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.